🚀 About WRITER

WRITER is the AI platform built for the world’s most ambitious marketing and revenue teams, bringing the frontier of AI to the people who drive enterprise growth. With WRITER, autonomous agents work across the buyer journey, continuously executing on market signals and enterprise intelligence to grow revenue faster. Every agent action and asset is grounded in trust, built on an Enterprise Brain that unites brand, product, and company knowledge with fiduciary-grade governance and compliance.

Fortune 500 companies are transforming how they go to market with WRITER, including 3 of the top 5 US banks, 2 of the 5 largest global CPG conglomerates, and 4 of the top 5 US healthcare insurers. Founded in 2020, WRITER is backed by investors including Insight Partners, ICONIQ, Radical Ventures, Premji Invest, Balderton, B Capital, Salesforce Ventures, Adobe Ventures, Accenture, Citi Ventures, IBM Ventures, WndrCo, and others. Learn more at WRITER.com.

📐 About the role

AI research at WRITER isn't just about publishing papers — it's about building the scientific foundation that powers some of the most ambitious enterprise AI deployments in the world. As an AI research scientist, you'll be at the center of that work. You'll drive a high-impact research agenda focused on large language models, agentic reasoning, and the system-level capabilities that make AI genuinely useful at enterprise scale. This is a rare opportunity to do research that matters twice over — advancing the field and shipping directly into products used by hundreds of thousands of people every day.

We're at an inflection point. Enterprises are moving from experimenting with AI to deeply embedding it across their operations, and WRITER's models are the engine making that possible. The work you do here — on post-training, planning, multi-step reasoning, and agentic workflows — will directly shape how the next generation of enterprise AI behaves, performs, and scales. You'll have the resources, infrastructure, and cross-functional support to pursue ambitious ideas and bring them to life quickly. This role is hybrid, based out of our San Francisco or New York City hub.

You'll report to our VP of AI research. Please note: role is open to Senior, Staff, and Sr. Staff applicants

We are open to hiring for this role at multiple levels, with compensation ranges scaled to match your experience, expertise, and the scope of the role.

🦸🏻‍♀️ What you'll do

Lead an independent, high-impact research agenda on large language models and agentic systems, owning projects from early hypothesis through model training, evaluation, and production deployment

Design and execute large-scale post-training experiments using supervised fine-tuning, reinforcement learning from human feedback (RLHF), RLAIF, DPO, and emerging alignment techniques — with a focus on improving multi-step reasoning, planning, and tool use in enterprise agentic workflows

Build novel evaluation benchmarks and methodologies that push beyond existing limitations, establishing rigorous measures for how well models perform on complex, real-world enterprise tasks

Develop scalable data synthesis and curation pipelines that generate the high-quality training signal driving model improvement — including LLM-as-judge frameworks, synthetic data generation, and adversarial dataset construction

Shape WRITER's model architecture and training roadmap by translating your research insights into concrete improvements to our enterprise-grade LLMs, working hand-in-hand with research engineering and product teams

Publish and present original research at top-tier venues — NeurIPS, ICLR, ICML, ACL, and others — representing WRITER at the frontier of the field and contributing to the broader scientific community

Mentor and uplevel fellow researchers and engineers on the team, helping set a high bar for scientific rigor, experimental design, and research culture

⭐️ What you need

7+ years of hands-on ML research experience, with deep expertise in large language model pre-training and post-training — you've trained models at scale, debugged distributed jobs, and shipped improvements that made a measurable difference